MLOPS Training in Hyderabad
- Real-Time Projects
- Internship Till You Placed
- One-One Mentorship
MLops Training in Hyderabad
Batch Details
| Trainer Name | Madhumathi, Dr. Prasad |
| Trainer Experience | 10+ Years, 20+ Years |
| Next Batch Date | 25th Sep 2024 (8:00 AM IST) |
| Training Modes | Classroom Training (Hyderabad), Online Training |
| Course Duration | 3 Months |
| Call us at | +91 9885044555 |
| Email Us at | genaimasters@gmail.com |
| Demo Class Details | Click Here to Chat on Whatsapp |
MLops Training in Hyderabad
Course Curriculum
- What is MLOPS, Different stages in MLOPS, ML project lifecycle, Job Roles in MLOPS
- What is Development stage of an ML Workflow, Pipelines and steps,
- Artifacts, Materializers, Parameters & Settings.
- Stacks & components, Orchestrators, Artifact stores, Flavors etc.
- ML Server infrastructure, Server deployment, Metadata tracking,
- Collaborations, Dashboards
Trainer Details - MLOPS Training in Hyderabad
Ms. Madhumathi
Principal Data Scientist & Generative AI Strategist
10+ Years of Experience
About the Tutor
As an industry expert in Generative AI and an accomplished lead trainer, she is dedicated to advancing students’ careers by integrating real-time applications into Data Science and Generative AI education.
Mr. Prasad
Generative AI Authority & Principal Data Scientist
20+ years of Experience
About the Tutor
Our trainer is an Expert in AI specialist and Lead Data Scientist with deep expertise in Machine Learning, Deep Learning, NLP, Python/R, along with Statistical, Biological, and Panel Analysis.
The trainer has extensive experience in the healthcare and medical fields, having managed projects across the US, UK, Australia, and Canada. He possesses strong expertise in image processing for various diseases, utilizing Generative AI techniques.
Why Choose us
Expert Instructors
The training is conducted by experienced professionals with deep expertise in MLOps and machine learning. These instructors bring real-time knowledge and practical insights, providing high-quality instruction and support throughout the course.
Hands-On Experience
Generative AI Masters emphasizes practical learning with real-world projects and case studies. This hands-on approach allows participants to apply MLOps concepts in practical scenarios, enhancing their skills and confidence in managing machine learning models.
Comprehensive Curriculum
Generative AI Masters offers a well-structured MLOps training program that covers all essential aspects of machine learning operations, from foundational concepts to advanced techniques. The curriculum includes hands-on experience with tools like Docker, Kubernetes, and cloud platforms, ensuring a thorough understanding of MLOps.
Industry-Relevant Training
The course content is designed to align with current industry standards and best practices. This ensures that participants gain relevant skills that are highly sought after in the job market.
Personalized Support
Generative AI Masters offers personalized mentorship and support to each participant. This includes guidance on projects, career advice, and assistance with any challenges faced during the training.
Strong Placement Support
Generative AI Masters has a robust placement support system, including career counseling and job placement assistance. The institute’s network with industry leaders helps students find relevant job opportunities in the MLOps field.
Flexible Learning Options
The institute provides flexible learning options, including online and offline classes, to accommodate different schedules and learning preferences. This makes it easier for professionals to balance their studies with work commitments.
Positive Reviews and Success Stories
Many past students have successfully advanced their careers in MLOps after completing the training at Generative AI Masters. Positive feedback and success stories reflect the effectiveness and impact of the program.
Modes - Generative AI Course in Hyderabad
Classroom Training
- Interactive Face-to-Face Teaching
- Industry Expert Trainers
- Instant Feedback
- Collaborative Tasks
- Hands-on Industry Projects
- Group Discussions
- Covers Advanced Topics
Online Training
- Virtual Learning Sessions
- Daily Session Recordings
- Instructor Support
- Interactive Webinars
- Digital Learning Modules
- Online Practical Labs
- Flexible Learning Schedules
Corporate Training
- Customized Training Programs
- Daily Recordings
- Interactive Team Development
- Expert Instruction
- Industry-Relevant Content
- Performance Monitoring
- On-Site Workshops
What is MLops ?
- MLOps stands for Machine Learning Operations, blending machine learning with DevOps practices.
- It focuses on automating and streamlining the deployment of machine learning models into production.
- MLOps covers the entire machine learning lifecycle, from data preparation to model monitoring.
- It enhances collaboration between data scientists and IT operations teams.
- Continuous integration and continuous delivery (CI/CD) are key components of MLOps.
- MLOps ensures that machine learning models can be updated and scaled efficiently.
- It focuses on tracking models in production to identify and resolve issues such as data drift.
- The goal of MLOps is to make machine learning models more reliable and easier to manage in real-time applications.
Why is MLops Used?
- MLOps is used to automate the deployment of machine learning models into production.
- It ensures consistent and reliable updates to machine learning models.
- MLOps helps monitor models in production to quickly detect and fix issues.
- It enables collaboration between data scientists and IT operations teams.
- MLOps supports the continuous integration and delivery of machine learning models.
- It allows for efficient scaling of machine learning models in real-time applications.
- MLOps simplifies the challenges of data versioning and model updating.
- It improves the overall reliability and performance of machine learning systems.
About MLops
MLOps, short for Machine Learning Operations, is a practice that combines machine learning (ML) with DevOps to streamline and automate the process of deploying, monitoring, and managing machine learning models in production.
It bridges the gap between data scientists and IT operations, ensuring that machine learning models can be efficiently and reliably integrated into real-time applications.
MLOps includes the entire machine learning lifecycle, including data preparation, model training, deployment, monitoring, and retraining, allowing organizations to continuously deliver and improve ML-driven solutions.
The primary goal of MLOps is to enhance the collaboration between data science teams and operations teams, enabling faster experimentation, more reliable deployments, and better scalability.
By applying principles from DevOps, such as continuous integration and continuous delivery (CI/CD), MLOps ensures that models can be rapidly tested and deployed, with automated workflows reducing the risk of errors.
Additionally, MLOps emphasizes the importance of monitoring models in production to detect issues like data drift or performance degradation, allowing for timely interventions and model updates.
This approach not only accelerates the deployment of machine learning models but also ensures their long-term reliability and effectiveness in production environments.
Generative AI Masters in Hyderabad offers MLOps training designed to equip learners with the skills needed to operationalize machine learning models effectively.
The course covers essential tools and practices like Kubernetes, Docker, Jenkins, and cloud platforms such as AWS and Azure, ensuring participants gain hands-on experience in automating and managing ML pipelines.
With a curriculum that blends theory with practical application through real-world projects, Generative AI Masters prepares both beginners and professionals to excel in the fast-growing field of MLOps, guided by expert instructors who provide in-depth knowledge and industry insights.
Course Outline
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Tools Covered
Docker
For containerizing machine learning applications and ensuring consistent environments across different stages of deployment.
Kubernetes
For orchestrating and managing containerized applications at scale, including deploying and scaling ML models.
Jenkins
For automating the continuous integration and continuous delivery (CI/CD) pipelines for machine learning models.
Git
For version control and managing changes to code and model configurations.
Terraform
For infrastructure as code (IaC) to automate the provisioning and management of cloud resources.
MLflow
For tracking experiments, managing model versions, and facilitating model deployment.
Apache Airflow
For scheduling and monitoring workflows and pipelines in machine learning projects.
Prometheus and Grafana
For monitoring and visualizing metrics related to model performance and system health.
Kubeflow
For deploying and managing machine learning workflows on Kubernetes.
Skills developed post MLops training
- Proficiency in using MLOps tools like Docker and Kubernetes for model deployment and management.
- Ability to automate machine learning pipelines and workflows for efficient model updates.
- Understanding of continuous integration and continuous delivery (CI/CD) practices specific to machine learning.
- Knowledge of cloud platforms like AWS and Azure for deploying and scaling ML models.
- Experience in monitoring and maintaining machine learning models in production environments.
- Skills in managing data versioning and feature engineering to ensure model accuracy and performance.
- Capability to troubleshoot and resolve issues related to model drift and performance degradation.
- Familiarity with best practices for collaborating between data science and IT operations teams.
Job Opportunities
- MLOps Engineer: Focuses on deploying, managing, and scaling machine learning models in production environments, ensuring smooth integration between data science and IT operations.
- Machine Learning Operations Specialist : Works on optimizing and automating ML workflows, maintaining model performance, and implementing best practices in MLOps.
- Data Engineer: Involved in building and managing data pipelines that support machine learning models, ensuring data quality and availability for ML processes.
- DevOps Engineer: Specializes in integrating machine learning models into existing DevOps practices, including CI/CD pipelines and infrastructure management.
- ML Infrastructure Engineer: Designs and manages the infrastructure needed for running machine learning models, including cloud resources and on-premises systems.
- AI Operations Manager: Oversees the end-to-end management of machine learning operations, including model deployment, monitoring, and lifecycle management.
- Machine Learning Architect: Develops and designs the architecture for deploying and scaling machine learning models, focusing on system integration and performance.
- Data Scientist with MLOps Skills: Combines data science expertise with MLOps knowledge to develop and deploy models efficiently, ensuring they are production-ready and maintainable.
- Cloud Engineer with MLOps Focus: Manages cloud-based infrastructure for machine learning, implementing scalable solutions for model deployment and operations.
- Model Monitoring Specialist: Specializes in tracking and analyzing the performance of machine learning models in production, detecting issues like data drift and model degradation.
Key Points of MLops
- The MLOps training at Generative AI Masters focuses on integrating machine learning with DevOps practices.
- It provides hands-on experience with tools like Kubernetes, Docker, and Jenkins.
- The course covers end-to-end machine learning pipeline automation.
- Students learn about continuous integration and continuous deployment (CI/CD) for machine learning models.
- The training includes real-world projects to apply MLOps concepts in practical scenarios.
- The curriculum is designed to help participants master model monitoring and management.
- It offers insights into data versioning and feature engineering for robust ML models.
- Generative AI Masters' MLOps course includes training on cloud platforms like AWS and Azure.
- The program is suitable for both beginners and professionals looking to enhance their skills in MLOps.
- Expert instructors guide students through the complexities of operationalizing machine learning models.
Placement Program
Generative AI Masters offers a comprehensive placement program as part of its MLOps training, ensuring students transition seamlessly into the job market.
The program includes personalized career counseling, resume building, and interview preparation to ensure participants are well-equipped for job opportunities.
Additionally, Generative AI Masters leverages its strong network of industry connections to facilitate job placements, connecting students with top employers in the field of MLOps.
GenerativeAI Masters is a top institute in Hyderabad, known for its advanced training programs in machine learning and artificial intelligence. With a focus on practical, hands-on learning and real-time applications, Generative AI Masters provides students with the skills and knowledge needed to excel in their careers.
- Career Counseling: Personalized guidance to help you identify and pursue the right career path.
- Resume Building: Assistance in creating professional resumes that highlight your skills and experience.
- Interview Preparation: Mock interviews and tips to help you succeed in job interviews.
- Job Search Support: Access to job listings and referrals to our network of partner companies.
Prerequisites
- Basic understanding of machine learning concepts and algorithms is required.
- Familiarity with programming languages such as Python is essential.
- Knowledge of version control systems like Git is beneficial for managing code and models.
- Experience with cloud platforms or containerization tools is helpful but not mandatory. Press Tab to write more...
Approximate Pay Scale in MLOPS Engineer
Entry-Level MLOps Engineer
Experience: 0-2 years Annual Salary Range: $70,000 - $100,000 Description: Entry-level roles typically involve supporting the deployment and management of machine learning models, basic automation of workflows, and learning the infrastructure and tools used in MLOps.
Mid-Level MLOps Engineer
Experience: 2-5 years Annual Salary Range: $100,000 - $140,000 Description: Mid-level MLOps engineers are responsible for designing and implementing pipelines, optimizing model performance, ensuring scalable deployment, and managing the infrastructure. They may also start taking on leadership roles in small teams.
Senior MLOps Engineer
Experience: 5+ years Annual Salary Range: $140,000 - $180,000+ Description: Senior engineers lead the design and development of complex MLOps pipelines, manage large-scale deployments, and oversee the integration of MLOps practices across the organization. They are often involved in mentoring junior staff and making strategic decisions.
Lead/Architect MLOps Roles
Experience: 7+ years Annual Salary Range: $180,000 - $220,000+ Description: In these roles, professionals are responsible for setting the strategic direction for MLOps practices, designing the architecture for machine learning infrastructure, and leading large teams. They also work closely with data science, engineering, and executive teams to align MLOps with business goals.
Market trend
- MLOps is rapidly gaining popularity as organizations seek to streamline the deployment and management of machine learning models.
- There is increasing demand for professionals skilled in MLOps due to the growing adoption of machine learning across industries.
- Companies are investing heavily in MLOps tools and technologies to improve the efficiency and scalability of their ML operations.
- The trend towards automation in MLOps is driven by the need to reduce manual intervention and errors in model deployment.
- Integration of MLOps with cloud platforms is becoming common, as businesses leverage cloud services for scalable ML infrastructure.
- Organizations are focusing on model monitoring and management to ensure model performance and address issues like data drift.
- There is a rise in the development of new MLOps frameworks and tools to address emerging challenges in machine learning operations.
- MLOps is increasingly being recognized as a critical component for successful AI projects, leading to its widespread adoption and growth in the market.
- Job roles related to MLOps are expanding, with positions such as MLOps engineers, data engineers, and machine learning operations specialists becoming more common in the job market.
- Companies are looking for MLOps professionals who can bridge the gap between data science and IT operations, highlighting the need for expertise in both areas for effective model management and deployment.
Generative AI Masters achievements
Our Great Achievements
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Certification
Earning a certification often involves passing an exam that tests understanding of key concepts, tools, and best practices in MLOps, such as continuous integration, continuous deployment (CI/CD), and model monitoring.
Microsoft Certified: Azure Data Scientist Associate
This certification focuses on managing machine learning models and data pipelines using Microsoft Azure. It covers key aspects of deploying, managing, and optimizing ML solutions in the Azure environment.
Google Professional Machine Learning Engineer
This certification demonstrates expertise in designing, building, and deploying ML models using Google Cloud Platform. It emphasizes practical skills in managing machine learning solutions and ensuring their scalability and performance.
AWS Certified Machine Learning – Specialty
This certification showcases proficiency in deploying and managing machine learning models on Amazon Web Services (AWS). It covers topics such as model optimization, deployment, and maintenance in the AWS ecosystem.
Certified MLOps Professional (CMOP)
The CMOP certification is specifically designed for MLOps practitioners. It focuses on best practices and tools for operationalizing machine learning models, including model deployment, monitoring, and lifecycle management.
Faqs
MLOps, or Machine Learning Operations, is a set of practices that combines machine learning with DevOps to streamline and automate the deployment, management, and monitoring of machine learning models in production environments.
MLOps training helps professionals gain skills in automating ML pipelines, managing model deployment, monitoring performance, and integrating ML models with existing IT infrastructure, leading to improved efficiency and scalability.
Basic understanding of machine learning concepts, familiarity with Python programming, and knowledge of version control systems like Git are recommended prerequisites. Experience with cloud platforms or containerization tools is helpful but not required.
The duration of MLOps training programs can vary, but typically they range from a 4 weeks to 6 Weeks, depending on the depth of the course and the learning format.
MLOps training often includes tools like Docker, Kubernetes, Jenkins, Git, MLflow, Apache Airflow, and cloud platforms such as AWS, Azure, and Google Cloud.
Yes, MLOps training can be suitable for beginners who have a basic understanding of machine learning and programming. Many programs are designed to cater to varying levels of experience.
Career opportunities include roles such as MLOps Engineer, Machine Learning Operations Specialist, Data Engineer, DevOps Engineer, ML Infrastructure Engineer, and AI Operations Manager.
Yes, Generative AI Masters provides placement assistance, including career counseling, resume building, interview preparation, and access to job opportunities through its industry network for more Details contact Gen Ai masters .
Yes, some MLOps training programs may prepare you for certifications such as the Microsoft Certified: Azure Data Scientist Associate, Google Professional Machine Learning Engineer, AWS Certified Machine Learning – Specialty, and Certified MLOps Professional (CMOP).
Yes, many MLOps training programs are offered online, providing flexibility to learn from anywhere. These programs often include virtual classrooms, recorded sessions, and online resources.
